OptimalNoBins = function(Data){
#OptimalNrOfBins = OptNrOfBinsV2(Data)
# DESCRIPTION
# Berechung der optimalen Anzahl von Bins fuer ein Histogramm
# nach Keating/Scott 99
# INPUT
# Data [1:n] vector
# OUTPUT
# OptimalNrOfBins die bestmoegliche ANzahl von Bins, minimal jedoch 10
# Verwendung fuer hist(data,OptimalNrOfBins);
#Anzahl vorhandene Daten
#MT 2019
if(is.matrix(Data)) nData <- colSums(!is.nan(Data))
if(is.vector(Data)) nData <- sum(!is.nan(Data))
if(nData<1){
optNrOfBins<-0
}else{
sigma<-sd(Data,na.rm=TRUE)
p<-quantile(Data,c(0.25,0.75),type=8,na.rm = T)
interquartilRange<-p[2]-p[1]
sigmaSir<-min(sigma,interquartilRange/1.349)
optBinWidth<-3.49*sigmaSir/(nData)^(1/3)
if(optBinWidth>0){
optNrOfBins<-max(ceiling((max(Data,na.rm=TRUE)-min(Data,na.rm=TRUE))/optBinWidth),10)
}else{
optNrOfBins<-10
}
}
return (optNrOfBins)
}
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